Recently, various digital photographing devices (such as digital cameras and video cameras) are continuously developed and improved. With their constantly enhanced image quality, ever-reducing product size, and steadily lowering prices, the digital photographing devices are more and more popular, and become indispensable tools for many people in their daily lives and at work.
For example, a mobile phone having a photographing function is generally provided with a CCD or CMOS image capturing unit for capturing images, and a small LCD screen for showing the captured images to a user. Generally, when the mobile phone is used to photograph a scene, the images taken are stored in a memory card inserted in the mobile phone. Then, when the user selects an image listed on the LCD screen of the mobile phone, the mobile phone reads the selected image from the memory card and re-encodes the selected image into a minified image (i.e., a thumbnail) for display on the LCD screen, allowing the user to execute various operations (such as minification, magnification, drag, or page-size adjustment) to the minified image on the LCD screen. Normally, the size of an image stored in a digital photographing device may be several megabytes (such as 1.2 MB) or hundreds of kilobytes (such as 120 KB), and the image is stored in a storage device (such as a memory card, a hard disk, or a flash drive) of the digital photographing device. Once the user selects the image, the digital photographing device reads the image from the storage device and re-encodes the original image into a minified image having a smaller size of about tens of kilobytes (such as 75 KB) or several kilobytes (such as 7.5 KB) for display on a small LCD screen, so that the user can browse the minified image and execute various operations (such as minification, magnification, drag, or page-size adjustment) to the minified image according to actual needs.
As the image resolution and the capturing speed of digital photographing devices are continuously increased, a variety of digital photographing devices have been widely used in various professional fields including criminal investigation, biological research, medical science, astrology, etc., for preserving important evidence, such as key clues in criminal cases, exhibits for use as evidence, and images at crime scenes; new findings or experimental results in biological science; and medical X-ray images, computerized tomography images, and other data helpful for diagnosis by medical workers. Therefore, it is an important issue for researchers in each professional field to figure out how to preserve critical evidence in laboratories or in other research fields and save the evidence in a digital image format, so as to facilitate review or comparison of important data in subsequent experiments or researches. It is also important to effectively decrease the distortion of digital images during magnification, so that the digital images displayed have high resolution and are easy to identify, thereby enabling analysis and determination of characteristics shown in the digital images.
Furthermore, after conducting researches on the technologies for magnifying images and videos for years, image processing professionals and relevant designers have developed various new theories and applications continuously, from the initial linear magnification to the later edge-based magnification, wherein frequently used linear magnification techniques include bilinear interpolation, bicubic interpolation, Lanzcos algorithm, etc., while a typical example of edge-based magnification is the new edge-directed interpolation (NEDI). However, there are still some shortcomings in the foregoing magnification techniques. For example, the linear magnification tends to cause a blocky-edge effect, loss of details, and blurry edges. On the other hand, the edge-based magnification, in which interpolation is executed along image edges along a gradient direction to partly solve the problems of blocky or blurry edges, tends to produce incorrect interpolation results, especially in an image region having rich details and messy edges, due to an inaccurate edge direction as the edge-based magnification is highly dependent on the accuracy of the edge direction. Moreover, the edge-based magnification relies on a considerable amount of calculation to maintain the accuracy of results, but the efficiency of image processing may be lowered accordingly. Finally, since most of the traditional magnification techniques use weighted summation of neighborhood pixels to perform interpolation, in which the weighted summation produces a low-pass filtering effect on the original image, some sharpness and detail information (i.e., the high-frequency portion) of the original image is inevitably lost after the original image is magnified. Therefore, in order to recover the quality of the original image, image processing professionals usually perform certain enhancement and restoration processes on the magnified image. However, the enhancement and restoration processes result in new defects such as overshoot and the ringing effect.
Therefore, it is important for image processing professionals and relevant designers to develop a new technology for magnifying images, so that when low-resolution images and videos are magnified and shown on a high-resolution video-frequency apparatus, clear digital images are displayed to facilitate identification of characteristics shown in the images.